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ConceptNet 5.5: An Open Multilingual Graph of General Knowledge

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arxiv 1612.03975 v2 pith:SW2LY4AB submitted 2016-12-12 cs.CL

Robyn Speer , Joshua Chin , Catherine Havasi This is my paper
classification cs.CL
keywords knowledgeconceptnetlanguagewordapplicationsdistributionalembeddingsgeneral
verification ladder T0 review T1 audit T2 compute T3 formal
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Machine learning about language can be improved by supplying it with specific knowledge and sources of external information. We present here a new version of the linked open data resource ConceptNet that is particularly well suited to be used with modern NLP techniques such as word embeddings. ConceptNet is a knowledge graph that connects words and phrases of natural language with labeled edges. Its knowledge is collected from many sources that include expert-created resources, crowd-sourcing, and games with a purpose. It is designed to represent the general knowledge involved in understanding language, improving natural language applications by allowing the application to better understand the meanings behind the words people use. When ConceptNet is combined with word embeddings acquired from distributional semantics (such as word2vec), it provides applications with understanding that they would not acquire from distributional semantics alone, nor from narrower resources such as WordNet or DBPedia. We demonstrate this with state-of-the-art results on intrinsic evaluations of word relatedness that translate into improvements on applications of word vectors, including solving SAT-style analogies.

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Cited by 5 Pith papers

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    Using ConceptNet-augmented prompts, ConceptBot reports 87% vs 31% success on implicit tasks and 76% vs 15% on risk-aware tasks over a re-implemented SayCan baseline, with an 80% SafeAgentBench score.

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